Script to reproduce years based on a model trained with random points¶
Importing¶
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import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import TransformedTargetRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.metrics import root_mean_squared_error as rmse
from tqdm import tqdm
import dill
import random
import salishsea_tools.viz_tools as sa_vi
Datasets Preparation¶
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def datasets_preparation(dataset, dataset2):
drivers = np.stack([np.ravel(dataset['Temperature_(0m-15m)']),
np.ravel(dataset['Temperature_(15m-100m)']),
np.ravel(dataset['Salinity_(0m-15m)']),
np.ravel(dataset['Salinity_(15m-100m)']),
np.ravel(dataset2['Summation_of_solar_radiation']),
np.ravel(dataset2['Mean_wind_speed']),
np.ravel(dataset2['Mean_air_temperature'])
])
indx = np.where(~np.isnan(drivers).any(axis=0))
drivers = drivers[:,indx[0]]
diat = np.ravel(dataset['Diatom'])
diat = diat[indx[0]]
return(drivers, diat, indx)
Regressor¶
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def regressor (inputs, targets):
inputs = inputs.transpose()
# Regressor
X_train, _, y_train, _ = train_test_split(inputs, targets, train_size=0.35)
model = ExtraTreesRegressor()
model = make_pipeline(StandardScaler(), model)
regr = BaggingRegressor(model, n_estimators=12, n_jobs=4).fit(X_train, y_train)
return (regr)
Regressor 2¶
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def regressor2 (inputs, targets, variable_name):
inputs2 = inputs.transpose()
outputs_test = regr.predict(inputs2)
m = scatter_plot(targets, outputs_test, variable_name)
r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
rms = rmse(targets, outputs_test)
return (r, rms, m)
Regressor 3¶
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def regressor3 (inputs, targets):
inputs2 = inputs.transpose()
outputs_test = regr.predict(inputs2)
# compute slope m and intercept b
m, b = np.polyfit(targets, outputs_test, deg=1)
r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
rms = rmse(targets, outputs_test)
return (r, rms, m)
Regressor 4¶
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def regressor4 (inputs, targets, variable_name):
inputs2 = inputs.transpose()
outputs = regr.predict(inputs2)
# Post processing
indx2 = np.full((len(diat_i.y)*len(diat_i.x)),np.nan)
indx2[indx[0]] = outputs
model = np.reshape(indx2,(len(diat_i.y),len(diat_i.x)))
m = scatter_plot(targets, outputs, variable_name + str(dates[i].date()))
# Preparation of the dataarray
model = xr.DataArray(model,
coords = {'y': diat_i.y, 'x': diat_i.x},
dims = ['y','x'],
attrs=dict( long_name = variable_name + "Concentration",
units="mmol m-2"),)
plotting3(targets, model, diat_i, variable_name)
Printing¶
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def printing (targets, outputs, m):
print ('The amount of data points is', outputs.size)
print ('The slope of the best fitting line is ', np.round(m,3))
print ('The correlation coefficient is:', np.round(np.corrcoef(targets, outputs)[0][1],3))
print (' The mean square error is:', rmse(targets,outputs))
Scatter Plot¶
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def scatter_plot(targets, outputs, variable_name):
# compute slope m and intercept b
m, b = np.polyfit(targets, outputs, deg=1)
printing(targets, outputs, m)
fig, ax = plt.subplots(2, figsize=(5,10), layout='constrained')
ax[0].scatter(targets,outputs, alpha = 0.2, s = 10)
lims = [np.min([ax[0].get_xlim(), ax[0].get_ylim()]),
np.max([ax[0].get_xlim(), ax[0].get_ylim()])]
# plot fitted y = m*x + b
ax[0].axline(xy1=(0, b), slope=m, color='r')
ax[0].set_xlabel('targets')
ax[0].set_ylabel('outputs')
ax[0].set_xlim(lims)
ax[0].set_ylim(lims)
ax[0].set_aspect('equal')
ax[0].plot(lims, lims,linestyle = '--',color = 'k')
h = ax[1].hist2d(targets,outputs, bins=100, cmap='jet',
range=[lims,lims], cmin=0.1, norm='log')
ax[1].plot(lims, lims,linestyle = '--',color = 'k')
# plot fitted y = m*x + b
ax[1].axline(xy1=(0, b), slope=m, color='r')
ax[1].set_xlabel('targets')
ax[1].set_ylabel('outputs')
ax[1].set_aspect('equal')
fig.colorbar(h[3],ax=ax[1], location='bottom')
fig.suptitle(variable_name)
plt.show()
return (m)
Plotting¶
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def plotting(variable, name):
plt.plot(years,variable, marker = '.', linestyle = '')
plt.xlabel('Years')
plt.ylabel(name)
plt.show()
Plotting 2¶
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def plotting2(variable,title):
fig, ax = plt.subplots()
scatter= ax.scatter(dates,variable, marker='.', c=pd.DatetimeIndex(dates).month)
ax.legend(handles=scatter.legend_elements()[0], labels=['February','March','April'])
fig.suptitle('Daily ' + title + ' (15 Feb - 30 Apr)')
fig.show()
Plotting 3¶
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def plotting3(targets, model, variable, variable_name):
fig, ax = plt.subplots(2,2, figsize = (10,15))
cmap = plt.get_cmap('cubehelix')
cmap.set_bad('gray')
variable.plot(ax=ax[0,0], cmap=cmap, vmin = targets.min(), vmax =targets.max(), cbar_kwargs={'label': variable_name + ' Concentration [mmol m-2]'})
model.plot(ax=ax[0,1], cmap=cmap, vmin = targets.min(), vmax = targets.max(), cbar_kwargs={'label': variable_name + ' Concentration [mmol m-2]'})
((variable-model) / variable * 100).plot(ax=ax[1,0], cmap=cmap, cbar_kwargs={'label': variable_name + ' Concentration [percentage]'})
plt.subplots_adjust(left=0.1,
bottom=0.1,
right=0.95,
top=0.95,
wspace=0.35,
hspace=0.35)
sa_vi.set_aspect(ax[0,0])
sa_vi.set_aspect(ax[0,1])
sa_vi.set_aspect(ax[1,0])
ax[0,0].title.set_text(variable_name + ' (targets)')
ax[0,1].title.set_text(variable_name + ' (outputs)')
ax[1,0].title.set_text('targets - outputs')
ax[1,1].axis('off')
fig.suptitle(str(dates[i].date()))
plt.show()
Training (Random Points)¶
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ds = xr.open_dataset('/data/ibougoudis/MOAD/files/integrated_model_var_old.nc')
ds2 = xr.open_dataset('/data/ibougoudis/MOAD/files/external_inputs.nc')
ds = ds.isel(time_counter = (np.arange(0, len(ds.time_counter),2)),
y=(np.arange(ds.y[0], ds.y[-1], 5)),
x=(np.arange(ds.x[0], ds.x[-1], 5)))
ds2 = ds2.isel(time_counter = (np.arange(0, len(ds2.time_counter),2)),
y=(np.arange(ds2.y[0], ds2.y[-1], 5)),
x=(np.arange(ds2.x[0], ds2.x[-1], 5)))
dates = pd.DatetimeIndex(ds['time_counter'].values)
drivers, diat, _ = datasets_preparation(ds, ds2)
regr = regressor(drivers, diat)
Other Years (Anually)¶
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years = range (2007,2024)
r_all = []
rms_all = []
slope_all = []
for year in tqdm(range (2007,2024)):
dataset = ds.sel(time_counter=str(year))
dataset2 = ds2.sel(time_counter=str(year))
drivers, diat, _ = datasets_preparation(dataset, dataset2)
r, rms, m = regressor2(drivers, diat, 'Diatom ' + str(year))
r_all.append(r)
rms_all.append(rms)
slope_all.append(m)
plotting(np.transpose(r_all), 'Correlation Coefficient')
plotting(np.transpose(rms_all), 'Root Mean Square Error')
plotting (np.transpose(slope_all), 'Slope of the best fitting line')
0%| | 0/17 [00:00<?, ?it/s]
The amount of data points is 70794 The slope of the best fitting line is 0.79 The correlation coefficient is: 0.943 The mean square error is: 0.055718561999310606
6%|▌ | 1/17 [01:07<18:03, 67.71s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.778 The correlation coefficient is: 0.926 The mean square error is: 0.05640590600520877
12%|█▏ | 2/17 [02:08<15:56, 63.74s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.79 The correlation coefficient is: 0.943 The mean square error is: 0.06900807497777764
18%|█▊ | 3/17 [03:08<14:26, 61.93s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.764 The correlation coefficient is: 0.937 The mean square error is: 0.05398620175081627
24%|██▎ | 4/17 [04:08<13:15, 61.19s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.838 The correlation coefficient is: 0.952 The mean square error is: 0.049096458547891166
29%|██▉ | 5/17 [05:11<12:21, 61.81s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.807 The correlation coefficient is: 0.946 The mean square error is: 0.053694577698207104
35%|███▌ | 6/17 [06:10<11:10, 60.97s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.778 The correlation coefficient is: 0.944 The mean square error is: 0.06381927261420603
41%|████ | 7/17 [07:09<10:02, 60.26s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.775 The correlation coefficient is: 0.922 The mean square error is: 0.05772809530113693
47%|████▋ | 8/17 [08:08<08:59, 59.97s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.774 The correlation coefficient is: 0.943 The mean square error is: 0.05384693281337726
53%|█████▎ | 9/17 [09:08<07:58, 59.81s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.828 The correlation coefficient is: 0.955 The mean square error is: 0.052335081889689966
59%|█████▉ | 10/17 [10:15<07:15, 62.15s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.799 The correlation coefficient is: 0.935 The mean square error is: 0.04892873500306341
65%|██████▍ | 11/17 [11:15<06:08, 61.41s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.747 The correlation coefficient is: 0.917 The mean square error is: 0.06495627808226698
71%|███████ | 12/17 [12:14<05:03, 60.65s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.779 The correlation coefficient is: 0.925 The mean square error is: 0.06714903125882138
76%|███████▋ | 13/17 [13:14<04:01, 60.49s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.749 The correlation coefficient is: 0.931 The mean square error is: 0.07902781067276396
82%|████████▏ | 14/17 [14:13<03:00, 60.18s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.831 The correlation coefficient is: 0.948 The mean square error is: 0.05634470354228901
88%|████████▊ | 15/17 [15:14<02:00, 60.19s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.79 The correlation coefficient is: 0.934 The mean square error is: 0.05393667584524409
94%|█████████▍| 16/17 [16:13<00:59, 59.99s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.762 The correlation coefficient is: 0.927 The mean square error is: 0.06480253521058248
100%|██████████| 17/17 [17:13<00:00, 60.80s/it]
Other Years (Daily)¶
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r_all2 = np.array([])
rms_all2 = np.array([])
slope_all2 = np.array([])
for i in tqdm(range (0, len(ds.time_counter))):
dataset = ds.isel(time_counter=i)
dataset2 = ds2.isel(time_counter=i)
drivers, diat, _ = datasets_preparation(dataset, dataset2)
r, rms, m = regressor3(drivers, diat)
r_all2 = np.append(r_all2,r)
rms_all2 = np.append(rms_all2,rms)
slope_all2 = np.append(slope_all2,m)
plotting2(r_all2, 'Correlation Coefficients')
plotting2(rms_all2, 'Root Mean Square Errors')
plotting2(slope_all2, 'Slope of the best fitting line')
2%|▏ | 15/640 [15:13<10:34:15, 60.89s/it]
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) Cell In[14], line 12 8 dataset2 = ds2.isel(time_counter=i) 10 drivers, diat, _ = datasets_preparation(dataset, dataset2) ---> 12 r, rms, m = regressor3(drivers, diat) 14 r_all2 = np.append(r_all2,r) 15 rms_all2 = np.append(rms_all2,rms) Cell In[5], line 5, in regressor3(inputs, targets) 1 def regressor3 (inputs, targets): 3 inputs2 = inputs.transpose() ----> 5 outputs_test = regr.predict(inputs2) 7 # compute slope m and intercept b 8 m, b = np.polyfit(targets, outputs_test, deg=1) File ~/conda_envs/analysis-ilias/lib/python3.11/site-packages/sklearn/ensemble/_bagging.py:1191, in BaggingRegressor.predict(self, X) 1188 # Parallel loop 1189 n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs) -> 1191 all_y_hat = Parallel(n_jobs=n_jobs, verbose=self.verbose)( 1192 delayed(_parallel_predict_regression)( 1193 self.estimators_[starts[i] : starts[i + 1]], 1194 self.estimators_features_[starts[i] : starts[i + 1]], 1195 X, 1196 ) 1197 for i in range(n_jobs) 1198 ) 1200 # Reduce 1201 y_hat = sum(all_y_hat) / self.n_estimators File ~/conda_envs/analysis-ilias/lib/python3.11/site-packages/sklearn/utils/parallel.py:67, in Parallel.__call__(self, iterable) 62 config = get_config() 63 iterable_with_config = ( 64 (_with_config(delayed_func, config), args, kwargs) 65 for delayed_func, args, kwargs in iterable 66 ) ---> 67 return super().__call__(iterable_with_config) File ~/conda_envs/analysis-ilias/lib/python3.11/site-packages/joblib/parallel.py:1952, in Parallel.__call__(self, iterable) 1946 # The first item from the output is blank, but it makes the interpreter 1947 # progress until it enters the Try/Except block of the generator and 1948 # reach the first `yield` statement. This starts the aynchronous 1949 # dispatch of the tasks to the workers. 1950 next(output) -> 1952 return output if self.return_generator else list(output) File ~/conda_envs/analysis-ilias/lib/python3.11/site-packages/joblib/parallel.py:1595, in Parallel._get_outputs(self, iterator, pre_dispatch) 1592 yield 1594 with self._backend.retrieval_context(): -> 1595 yield from self._retrieve() 1597 except GeneratorExit: 1598 # The generator has been garbage collected before being fully 1599 # consumed. This aborts the remaining tasks if possible and warn 1600 # the user if necessary. 1601 self._exception = True File ~/conda_envs/analysis-ilias/lib/python3.11/site-packages/joblib/parallel.py:1707, in Parallel._retrieve(self) 1702 # If the next job is not ready for retrieval yet, we just wait for 1703 # async callbacks to progress. 1704 if ((len(self._jobs) == 0) or 1705 (self._jobs[0].get_status( 1706 timeout=self.timeout) == TASK_PENDING)): -> 1707 time.sleep(0.01) 1708 continue 1710 # We need to be careful: the job list can be filling up as 1711 # we empty it and Python list are not thread-safe by 1712 # default hence the use of the lock KeyboardInterrupt:
Daily Maps¶
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maps = random.sample(range(0,len(ds.time_counter)),10)
for i in tqdm(maps):
dataset = ds.isel(time_counter=i)
dataset2 = ds2.isel(time_counter=i)
drivers, diat, indx = datasets_preparation(dataset, dataset2)
diat_i = dataset['Diatom']
regressor4(drivers, diat, 'Diatom ')
0%| | 0/10 [00:00<?, ?it/s]
The amount of data points is 1863 The slope of the best fitting line is 0.721 The correlation coefficient is: 0.917 The mean square error is: 0.05682262296005913
10%|█ | 1/10 [01:02<09:26, 62.94s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.791 The correlation coefficient is: 0.926 The mean square error is: 0.02917307477306278
20%|██ | 2/10 [02:11<08:47, 65.99s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.853 The correlation coefficient is: 0.894 The mean square error is: 0.023679885789193063
30%|███ | 3/10 [03:30<08:24, 72.08s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.738 The correlation coefficient is: 0.79 The mean square error is: 0.03089873869329355
40%|████ | 4/10 [04:44<07:18, 73.00s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.581 The correlation coefficient is: 0.64 The mean square error is: 0.06719042531980075
50%|█████ | 5/10 [05:54<05:58, 71.72s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.599 The correlation coefficient is: 0.806 The mean square error is: 0.08878353692556587
60%|██████ | 6/10 [07:07<04:49, 72.36s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.728 The correlation coefficient is: 0.855 The mean square error is: 0.03185442339583298
70%|███████ | 7/10 [08:17<03:34, 71.50s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.578 The correlation coefficient is: 0.832 The mean square error is: 0.08346775760164514
80%|████████ | 8/10 [09:25<02:21, 70.51s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.95 The correlation coefficient is: 0.818 The mean square error is: 0.051104704879814236
90%|█████████ | 9/10 [10:36<01:10, 70.54s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.673 The correlation coefficient is: 0.792 The mean square error is: 0.07310904781090435
100%|██████████| 10/10 [11:52<00:00, 71.24s/it] 100%|██████████| 10/10 [11:52<00:00, 71.24s/it]
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